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Editorial

Consistency Monitoring of Wind Turbine Blade Loads Based on MEMS Optical Fiber Sensing

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  • @ARTICLE{10.4108/ew.9387,
        author={Yong Xue  and Yang Li  and Xiangye Fan  and Binshan Xie  and Zhiyuan Ma  and Lin Lin  and Mengnan Cao },
        title={Consistency Monitoring of Wind Turbine Blade Loads Based on MEMS Optical Fiber Sensing},
        journal={EAI Endorsed Transactions on Energy Web},
        volume={12},
        number={1},
        publisher={EAI},
        journal_a={EW},
        year={2025},
        month={10},
        keywords={MEMS-FBG Sensors, Wind Turbine Blade Monitoring, Load Consistency Index, Strain-to-Load Calibration, SCADA Integration, Signal Drift Analysis , Anomaly Detection},
        doi={10.4108/ew.9387}
    }
    
  • Yong Xue
    Yang Li
    Xiangye Fan
    Binshan Xie
    Zhiyuan Ma
    Lin Lin
    Mengnan Cao
    Year: 2025
    Consistency Monitoring of Wind Turbine Blade Loads Based on MEMS Optical Fiber Sensing
    EW
    EAI
    DOI: 10.4108/ew.9387
Yong Xue 1, Yang Li 1, Xiangye Fan 1, Binshan Xie 1, Zhiyuan Ma 2, Lin Lin 2, Mengnan Cao 2,*
  • 1: State Power Investment Group Xinjiang Energy and Chemical Emin Co., Ltd
  • 2: Shanghai Power Equipment Research Institute
*Contact email: mengnam_cao987@outlook.com

Abstract

INTRODUCTION: This paper presents a comprehensive structural monitoring framework for wind turbine blades based on MEMS-FBG (micro-electro-mechanical systems—fiber Bragg grating) sensor fusion technology. OBJECTIVES: The system integrates high-resolution strain and vibration sensing across multiple blade segments, combined with real-time data processing, fault detection, and SCADA-level visualization. METHODS: A multilayered load consistency model is introduced, incorporating thermal compensation, strain-to-load calibration, and a novel consistency index (η) to quantify inter-blade aerodynamic symmetry. RESULTS: Experimental validation was conducted on a 2.0 MW wind turbine over a 60-day continuous monitoring campaign. Static calibration demonstrated a load reconstruction accuracy within ±3%, while dynamic data revealed a strong correlation between load and blade vibration (R ≥ 0.86). CONCLUSION: Fault simulation through pitch angle manipulation confirmed the system’s rapid alarm response within 3 seconds for major asymmetry events. Additionally, signal drift testing showed MEMS-FBG sensors exhibited 80–95% lower drift than conventional resistance strain gauges under rotating and EMI-intensive conditions.

Keywords
MEMS-FBG Sensors, Wind Turbine Blade Monitoring, Load Consistency Index, Strain-to-Load Calibration, SCADA Integration, Signal Drift Analysis , Anomaly Detection
Received
2025-10-15
Accepted
2025-10-15
Published
2025-10-15
Publisher
EAI
http://dx.doi.org/10.4108/ew.9387

Copyright © 2025 Yong Xue et al., licensed to EAI. This is an open-access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.

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